RI:Small: Leveraging Human Manipulation Skills to Advance Near Contact Robotic Grasping and In-Hand Stabilization

RI:Small：利用人类操纵技能推进近距离接触机器人抓取和手持稳定性

• 批准号: 1911050
• 负责人: Cindy Grimm
• 金额: $ 50万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911050&HistoricalAwards=false
• 关键词: RI; Small; Leveraging; Human; Manipulation

People are incredibly good at picking up and manipulating objects. Robots, however, still struggle with reliably picking up objects. The goal of this project is to improve robotic grasping by observing how humans respond to challenging grasping situations. The project's central idea is that humans strive for grasp success rather than trying to find the best possible grasp. Humans use a grasp that is likely to work, even if they have misjudged where the object is, what shape it is, how heavy it is, etc. Finding what these strategies are (in a form a robot can use) will improve robotic grasping in the "real world". The technical challenge addressed in this project is how to go from human demonstrations all the way to robot hand control strategies. First, human user study participants will demonstrate grasping with actual robotic hands by "puppeteering" the hands through a structured set of grasping and manipulation tasks. This demonstration will then be reconstructed in a physics simulator. This provides a rich set of data in an environment - grounded in physical reality - that algorithms can learn in. Finally, the data is put in a novel form that is ideally suited to capturing what happens as the hand comes into contact with the object in a form suitable for machine learning. The result is a learned set of near-contact robotic controllers that can be incorporated into existing grasp planning algorithms in order to improve robotic grasping.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们非常擅长拾取和操纵物体。然而，机器人仍然难以可靠地拾取物体。该项目的目标是通过观察人类如何应对具有挑战性的抓取情况来改善机器人抓取。该项目的中心思想是，人类努力争取抓取成功，而不是试图找到最好的抓取。 人类使用一种可能起作用的抓取，即使他们误判了物体的位置，形状，重量等。找到这些策略（以机器人可以使用的形式）将改善机器人在“真实的世界”中的抓取。该项目所面临的技术挑战是如何从人类演示一直到机器人手控制策略。首先，人类用户研究参与者将通过一组结构化的抓取和操纵任务来演示实际机器人手的抓取。这个演示将在物理模拟器中重建。这在一个基于物理现实的环境中提供了一组丰富的数据，算法可以在其中学习。最后，数据以一种新颖的形式呈现，这种形式非常适合于以适合机器学习的形式捕捉手与物体接触时发生的情况。其结果是一组习得的近接触机器人控制器，可以将其纳入现有的抓取规划算法中，以改善机器人抓取。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值进行评估，被认为值得支持和更广泛的影响审查标准。

项目成果

Improving Grasp Classification through Spatial Metrics Available from Sensors

通过传感器提供的空间指标改进抓取分类

• 发表时间: 2021
• 期刊: IEEE International Conference on Robotics and Automation
• 作者: Swenson, Nigel;Scott, Garrett;Bloch, Peter;Soni, Paresh;Nishat, Nuha;Asar, Anjali;Grimm, Cindy;Fern, Xiaoli;Balasubramanian, Ravi
• 通讯作者: Balasubramanian, Ravi

The Grasp Reset Mechanism: An Automated Apparatus for Conducting Grasping Trials

抓取重置机制：用于进行抓取试验的自动化装置

• 发表时间: 2024
• 期刊: International Conference on Robotics and Automation
• 作者: Kyle DuFrene, Keegan Nave

Benchmarking a Robot Hand’s Ability to Translate Objects Using Two Fingers

对机器人手使用两根手指翻译物体的能力进行基准测试

• DOI: 10.1109/lra.2021.3129131
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Morrow, John;Campbell, Joshua;Balasubramanian, Ravi;Grimm, Cindy
• 通讯作者: Grimm, Cindy